Change is Hard: A Closer Look at Subpopulation Shift (2302.12254v3)

Abstract: Machine learning models often perform poorly on subgroups that are underrepresented in the training data. Yet, little is understood on the variation in mechanisms that cause subpopulation shifts, and how algorithms generalize across such diverse shifts at scale. In this work, we provide a fine-grained analysis of subpopulation shift. We first propose a unified framework that dissects and explains common shifts in subgroups. We then establish a comprehensive benchmark of 20 state-of-the-art algorithms evaluated on 12 real-world datasets in vision, language, and healthcare domains. With results obtained from training over 10,000 models, we reveal intriguing observations for future progress in this space. First, existing algorithms only improve subgroup robustness over certain types of shifts but not others. Moreover, while current algorithms rely on group-annotated validation data for model selection, we find that a simple selection criterion based on worst-class accuracy is surprisingly effective even without any group information. Finally, unlike existing works that solely aim to improve worst-group accuracy (WGA), we demonstrate the fundamental tradeoff between WGA and other important metrics, highlighting the need to carefully choose testing metrics. Code and data are available at: https://github.com/YyzHarry/SubpopBench.

• The paper introduces a unified framework that categorizes subpopulation shifts into spurious correlations, class imbalance, attribute imbalance, and attribute generalization.
• It demonstrates that decoupling representation and classifier learning improves robustness for spurious correlations and class imbalance but not for attribute imbalances.
• Experiments on 12 datasets with 20 algorithms reveal that diverse pretraining and broader evaluation metrics are essential for addressing subpopulation shift challenges.

A Closer Examination of Subpopulation Shift

The paper "Change is Hard: A Closer Look at Subpopulation Shift" presents a comprehensive analysis of subpopulation shifts in machine learning models, primarily focusing on the robustness and adaptability of these models when confronted with variations in subpopulation distributions between training and testing phases. Through an extensive evaluation across domains, including vision, language, and healthcare, the authors explore and quantify different types of subpopulation shifts, proposing strategies to mitigate the resulting performance degradation.

Machine learning models often underperform on subpopulations that are underrepresented in the training process, a common phenomenon attributed to distributional shifts like subpopulation shifts. Such a shift occurs when there is a change in the proportion of subpopulations from training to deployment, which is a subset of the broader distribution shift problem. While spurious correlations are a widely recognized culprit, this research endeavors to dissect and understand various mechanisms underlying subpopulation shifts.

The paper introduces a unified framework for analyzing subpopulation shifts, which delineates the relationship between attributes and class labels. This framework aids in characterizing basic subpopulation shifts into spurious correlations, attribute imbalance, class imbalance, and attribute generalization, providing a structured approach to understanding these disparities.

Key findings of the paper include the observation that current state-of-the-art algorithms only enhance subgroup robustness for certain types of shifts, namely spurious correlations (SC) and class imbalance (CI), but struggle with attribute imbalance (AI) and attribute generalization (AG). This indicates a gap in existing methods in addressing more complex or less understood forms of subpopulation shifts. The paper's rigorous benchmark, consisting of 20 state-of-the-art algorithms evaluated across 12 datasets from vision, language, and healthcare domains, corroborates these findings and highlights the necessity for enhanced algorithmic designs that can account for all types of subpopulation shifts.

In diving deeper into methodological contributions, it becomes evident that algorithms that disentangle representation and classifier learning often yield superior performance, particularly in scenarios dominated by spurious correlations and class imbalance. This insight resonates with current discussions in the field about the sufficiency of ERM features for out-of-distribution generalization, as confirmed by strong numerical results signifying consistent performance gains by the decoupling strategy. However, even these advanced methodologies fall short in addressing attribute generalization, reaffirming the paper's claims of the need for advancing the state of algorithmic strategies.

The influence of model architectures and pretraining datasets, as explored in the experimental section, suggests that larger, more diverse pretraining datasets contribute positively to performance, particularly for challenging datasets that require generalization across unseen attributes. This finding aligns with the theoretical perspective that diverse pretraining can provide a more robust feature space, facilitating greater adaptability to varied subpopulations.

Finally, the practical implications of the research extend to the choice of model selection strategies and the emphasis on developing rigorous evaluation metrics. Worst-group accuracy has traditionally served as the primary benchmark for subgroup shifts, yet the paper advocates for a broader evaluation perspective that includes additional metrics like worst-case precision and calibration error, given the complex tradeoffs highlighted in correlation analyses.

In closing, the authors of this paper not only provide meaningful insights into current methodologies' limitations but also steer the focus towards addressing critical gaps in algorithmic robustness to subpopulation shifts. This research serves as a pointed reminder of the nuances within distributional shifts and the necessity for machine learning to evolve toward inclusivity and robustness across all data subpopulations. Future work could greatly benefit from targeted innovations, particularly in the realms of attribute generalization and algorithmic transparency within subpopulation contexts.